Software-based image processing using an associated machine learning model

ABSTRACT

Techniques for applying one or more machine learning models to a sub-region less than all of an image scene are described. An example is receiving first sub-region from an image; analyzing the received first sub-region of the image using the indicated least one machine learning model to perform the analyzing of the first sub-region of the scene; and outputting a result of the analyzing.

BACKGROUND

Computers can be taught to recognized aspects of an image. For example,aspects of an image may be classified using a machine learning model.Such classification may be invaluable for tasks such as autonomousdriving.

BRIEF DESCRIPTION OF DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates embodiments of an environment for machinelearning-based image processing on a subset of an image scene accordingto some embodiments.

FIG. 2 illustrates embodiments of an environment for machinelearning-based image processing on a subset of an image scene accordingto some embodiments.

FIG. 3 illustrates embodiments of an image scene at a first point intime.

FIG. 4 illustrates embodiments of the image scene at a second point intime.

FIG. 5 illustrates embodiments of a usage example of sub-region imageevaluation.

FIG. 6 is a flow diagram illustrating a method for independentlyanalyzing one or more sub-regions of an image using one or more MLmodels model according to some embodiments.

FIG. 7 is a block diagram of an illustrative operating environment inwhich machine learning models are trained and hosted according to someembodiments.

FIG. 8 illustrates an example provider network environment according tosome embodiments.

FIG. 9 is a block diagram of an example provider network that provides astorage service and a hardware virtualization service to customersaccording to some embodiments.

FIG. 10 is a block diagram illustrating an example computer system thatmay be used in some embodiments.

DETAILED DESCRIPTION

The present disclosure relates to methods, apparatus, systems, andnon-transitory computer-readable storage media for independentlyanalyzing one or more sub-regions of an image using one or more machinelearning models.

Traditionally, analysis of an image scene using a machine learning modelis performed on the entire image scene. While that may work well in somecircumstances, this one-size-fits all approach to analyzing the contentof an image scene may not produce the best results. For example, in animage scene of a large backyard where there are multiple desiredoutcomes, such recognizing a bear entering the backyard from a firstaccess point and recognizing a cat leaving the backyard at a secondaccess point, using the same model for these recognitions may not bereliable. Another example of a complex scene is one that captures both acash register of a register and workers preparing food.

FIG. 1 illustrates embodiments of an environment for machinelearning-based image processing on a subset of an image scene accordingto some embodiments. As illustrated, an image/video capture device 141such as a still or video camera captures one or more images to beprocessed using one or more machine learning models. In particular, theimage/video capture device 141 captures a scene per image. Using theexamples above, the scene encompasses a physical location such as abackyard, store, street, etc. The one or more images, or proper subsetsof a scene of such images, are sent to a provider network 100 comprisingone or more computing devices for machine learning-based imageprocessing. In this discussion, a proper subset of an image is a portionof the overall image scene instead of the entire image. In someembodiments, this content is sent via one or more intermediate networks106.

Image/video intake software 103 intakes one or more images (either stillor taken from a video), video, or a subset thereof. This intake mayinclude ingestion, storing the one or more images, video, or subsetthereof in image/video storage 111 (e.g., a database, local storage,etc.), encrypting the content, and/or indexing video.

One or more machine learning (ML) model(s) 133 are used to analyze aproper subset of an image scene. These ML model(s) 133 may be run inseveral ways. For example, the ML model(s) 133 may use a ML model host131 such as in some provider networks. For example, a ML model host 131may provide one or more of an operating system, runtime, etc. for the MLmodel(s) 133 to operate. In some embodiments, the ML model(s) 133 arerun on-premises (“on-prem”) as an application, etc. The ML model(s) 133are a part of a service of a provider network in some embodiments.

Depending upon the implementation, an image/video selector 105 of thecomputing device(s) 121 or an image/video selector 143 of theimage/video capture device 141 processes an image to extract, crop, orotherwise manipulate the image to make the proper subset (or subsets)available for analysis. In some embodiments, the extracted, cropped, orotherwise manipulated video is separately stored in the image/videostorage 111.

As noted above, in some embodiments, the computing device(s) 121 are apart of a provider network 100. The provider network 100 (or, “cloud”provider network) provides users with the ability to utilize one or moreof a variety of types of computing-related resources such as computeresources (e.g., executing virtual machine (VM) instances and/orcontainers, executing batch jobs, executing code without provisioningservers), data/storage resources (e.g., object storage, block-levelstorage, data archival storage, databases and database tables, etc.),network-related resources (e.g., configuring virtual networks includinggroups of compute resources, content delivery networks (CDNs), DomainName Service (DNS)), application resources (e.g., databases, applicationbuild/deployment services), access policies or roles, identity policiesor roles, machine images, routers and other data processing resources,etc. These and other computing resources may be provided as services,such as a hardware virtualization service that can execute computeinstances, a storage service that can store data objects, etc. The users(or “customers”) of provider networks 100 may utilize one or more useraccounts that are associated with a customer account, though these termsmay be used somewhat interchangeably depending upon the context of use.Users may interact with a provider network 100 across one or moreintermediate networks 106 (e.g., the internet) via one or moreinterface(s), such as through use of application programming interface(API) calls, via a console implemented as a website or application, etc.The interface(s) may be part of, or serve as a front-end to, a controlplane of the provider network 100 that includes “backend” servicessupporting and enabling the services that may be more directly offeredto customers.

For example, a cloud provider network (or just “cloud”) typically refersto a large pool of accessible virtualized computing resources (such ascompute, storage, and networking resources, applications, and services).A cloud can provide convenient, on-demand network access to a sharedpool of configurable computing resources that can be programmaticallyprovisioned and released in response to customer commands. Theseresources can be dynamically provisioned and reconfigured to adjust tovariable load. Cloud computing can thus be considered as both theapplications delivered as services over a publicly accessible network(e.g., the Internet, a cellular communication network) and the hardwareand software in cloud provider data centers that provide those services.

To provide these and other computing resource services, providernetworks 100 often rely upon virtualization techniques. For example,virtualization technologies may be used to provide users the ability tocontrol or utilize compute instances (e.g., a VM using a guest operatingsystem (O/S) that operates using a hypervisor that may or may notfurther operate on top of an underlying host O/S, a container that mayor may not operate in a VM, an instance that can execute on “bare metal”hardware without an underlying hypervisor), where one or multiplecompute instances can be implemented using a single electronic device.Thus, a user may directly utilize a compute instance (e.g., provided bya hardware virtualization service) hosted by the provider network toperform a variety of computing tasks. Additionally, or alternatively, auser may indirectly utilize a compute instance by submitting code to beexecuted by the provider network (e.g., via an on-demand code executionservice), which in turn utilizes a compute instance to execute thecode—typically without the user having any control of or knowledge ofthe underlying compute instance(s) involved.

For example, in various embodiments, a “serverless” function may includecode provided by a user or other entity—such as the provider networkitself—that can be executed on demand. Serverless functions may bemaintained within provider network 100 by an on-demand code executionservice and may be associated with a particular user or account or begenerally accessible to multiple users/accounts. A serverless functionmay be associated with a Uniform Resource Locator (URL), UniformResource Identifier (URI), or other reference, which may be used toinvoke the serverless function. A serverless function may be executed bya compute instance, such as a virtual machine, container, etc., whentriggered or invoked. In some embodiments, a serverless function can beinvoked through an application programming interface (API) call or aspecially formatted HyperText Transport Protocol (HTTP) request message.Accordingly, users can define serverless functions that can be executedon demand, without requiring the user to maintain dedicatedinfrastructure to execute the serverless function. Instead, theserverless functions can be executed on demand using resourcesmaintained by the provider network 100. In some embodiments, theseresources may be maintained in a “ready” state (e.g., having apre-initialized runtime environment configured to execute the serverlessfunctions), allowing the serverless functions to be executed in nearreal-time.

In the illustration of FIG. 1, circles with numbers inside indicate anexample of flow of operations. These operations occur after aconfiguration of: an image/video area selector 105 to extract or cropone or more sub-regions, the usage of a particular ML model of MLmodel(s) 133 to evaluate the sub-regions, etc. Note that any shapedefining a region may be utilized such as rectangles, circles, etc.

At circle 1, the image/video capture device 141 sends an image (or videofile) through an intermediate network 106 to be handled by theimage/video intake software 103. In this particular example, the intakeprocess includes sending the image (or video file) to image/videostorage 111 at circle 2. This allows for the image (or video file) to belater accessed for evaluation. In some embodiments, the image (or videofile) is received by image processing coordination (orchestration)software 107. The image processing coordination software 107 is anoverarching managing program that makes calls to various software piecesor services to facilitate the sub-region evaluation of an image scene.In some embodiments, the reception of the image (or video file) by thecomputing device(s) 121 causes the image processing coordinationsoftware 107 to initiate a flow of operations including one or more ofthose detailed.

At circle 3, the image/video area selector 105 is called. At circle 4,the image/video area selector 105 it extracts or crops the one or moresub-regions as configured. This extraction or cropping may happen on animage (or video file) retrieved from image/video storage 111, or on animage (or video file) more directly received (e.g., via the image/videointake software 103).

At circle 5, the resulting sub-region image(s) are sent to one or moreML model(s) 133 for evaluation. The one or more ML model(s) 133 performtheir evaluation at circle 6 and provide their result(s) to the imageprocessing coordinator 107.

FIG. 2 illustrates embodiments of an environment for machinelearning-based image processing on a subset of an image scene accordingto some embodiments. In this example, the image/video capture device 141is incorporated in the computing device 121. As illustrated, animage/video capture device 141 such as a still or video camera capturesone or more images to be processed using one or more machine learningmodels. In particular, the image/video capture device 141 captures a perimage a scene. Using the examples above, the scene encompasses aphysical location such as a backyard, store, street, etc. The one ormore images, or proper subsets of a scene of such images, are sent toone or more computing devices 121 for machine learning-based imageprocessing. In this discussion, a proper subset of an image is a portionof the overall image scene instead of the entire image.

The computing device 121 comprises computing device execution resources153 (such as processors, accelerators, etc.) that execute softwarestored in computing device memory 151 (e.g., RAM, disk, etc.) to providemachine learning-based image processing. Image/video intake software 103intakes one or more images (either still or taken from a video), video,or a subset thereof. This intake may include ingestion, storing the oneor more images, video, or subset thereof in image/video storage 111(e.g., local storage, etc.), encrypting the content, and/or indexingvideo.

One or more machine learning (ML) model(s) 133 are used to analyze aproper subset of an image scene. These ML model(s) 133 may be run inseveral ways. The computing device 121 provides one or more of anoperating system, runtime, etc. for the ML model(s) 133 to operate.

An image/video selector 105 of the computing device 121 processes animage to extract, crop, or otherwise manipulate the image to make theproper subset (or subsets) available for analysis.

As in the illustration of FIG. 1, circles with numbers inside indicatean example of flow of operations. These operations occur after aconfiguration of: an image/video area selector 105 to extract or cropone or more sub-regions, the usage of a particular ML model of MLmodel(s) 133 to evaluate the sub-regions, etc.

At circle 1, the image/video capture device 141 sends an image (or videofile) to be handled by the image/video intake software 103. In thisparticular example, the intake process includes sending the image (orvideo file) to image/video storage 111 at circle 2. This allows for theimage (or video file) to be later accessed for evaluation. In someembodiments, the image (or video file) is received by image processingcoordination software 107. The image processing coordination software107 is an overarching managing program that makes calls to varioussoftware pieces or services to facilitate the sub-region evaluation ofan image scene. In some embodiments, the reception of the image (orvideo file) by the computing device(s) 121 causes the image processingcoordination software 107 to initiate a flow of operations including oneor more of those detailed.

At circle 3, the image/video area selector 105 is called. At circle 4,the image/video area selector 105 it extracts or crops the one or moresub-regions as configured. This extraction or cropping may happen on animage (or video file) retrieved from image/video storage 111, or on animage (or video file) more directly received (e.g., via the image/videointake software 103).

At circle 5, the resulting sub-region image(s) are sent to one or moreML model(s) 133 for evaluation. The one or more ML model(s) 133 performtheir evaluation at circle 6 and provide their result(s) to the imageprocessing coordinator 107 which then returns the result at circle 7.

Note that the image entities of the computing device(s) 121 may beprovided as one or more services.

FIG. 3 illustrates embodiments of an image scene 301 at a first point intime. As shown, this image scene 301 has a plurality of sub-regions thatmay be of interest. However, what is of interest in one particularsub-region may not be of interest in other sub-regions. Unfortunately,if the image scene 301 is evaluated using a single ML model or multipleML models over the entire scene 301 the result of the evaluation will besuboptimal.

In sub-region 1 303 and sub-region 4 307, the entry and exit of abuilding are what is interesting. In particular, who comes in or out ofthe building. For example, is a person that is not authorized attemptingto access the building or is a person exiting the building at a veryunusual time?

Sub-region 3 305 covers a picnic area between the buildings. One maywant to analyze this area to see if wild animals are occupying the areaso that people in the buildings can be alerted to their presence, etc.

Sub-region 2 305 and sub-region 5 309 capture parking lots or separateportions of a single parking lot. One may want to analyze the vehiclesin these areas for unknown vehicles, a count of vehicles at a givenpoint in time, etc.

Each of the sub-regions 303-309 may be extracted (not destroying theimage) for evaluation by at least one ML model for each sub-region),cropped (thereby destroying the image), etc. In some embodiments, theselection of the sub-regions is provided to an image/video area selectorsuch as image/video area selector 143 or image/video area selector 105.

FIG. 4 illustrates embodiments of the image scene. This image scene 401depicts two conveyor belts 403A-B which move different types of objects.As shown, there are three different object types 421, 423, 425 which areshown having different shapes. In this example, a first sub-region 413covers the entirety of conveyor belt A 403A. However, multiple ML modelsare to be applied to this sub-region 413 to detect the different objecttypes. For example, model 1 is used to detect object type 1 421 andmodel 2 is used to detect object type 2 423.

Conveyor belt B 403B has multiple sub-regions to cover different aspectsof the belt. Sub-region 3 417 covers nearly the entire conveyor belt B403B whereas sub-region 2 415 just covers one corner. In sub-region 3417 only object type 2 is looked for and in sub-region 2 414 object type1 is looked for using ML model 1 and object type 3 is looked for usingML models 3 and 4. Shown in this illustration are objects that areshaded. These objects would not be found applying ML model 2 insub-region 3 417.

FIG. 5 illustrates embodiments of a usage example of sub-region imageevaluation. As shown, a ML model host 131 hosts ML models 501, 503, 507,and 509, respectively. These ML models correspond to the models of FIG.4. As shown, when an image scene is processed the sub-regions are carvedout. In this example, only sub-region 2 415 and sub-region 3 417 areshown. The shading in this illustration shows the parts of conveyor beltB 403B that are not included in a particular sub-region. By applying MLmodel 1 501 and ML model 2 503 to sub-region 2 415, an object of type 1and an object of type 2 are found as shown in sub-region 2 result 511.The application of ML model 3 507 followed by ML model 4 509 tosub-region 3 417 results in finding an object of type 3 as shown insub-region 3 result 513.

FIG. 6 is a flow diagram illustrating a method for independentlyanalyzing one or more sub-regions of an image using one or more MLmodels model according to some embodiments. Some or all of the aspectsof the method (or other processes described herein, or variations,and/or combinations thereof) are performed under the control of one ormore computer systems configured with executable instructions and areimplemented as code (e.g., executable instructions, one or more computerprograms, or one or more applications) executing collectively on one ormore processors, by hardware or combinations thereof. The code is storedon a computer-readable storage medium, for example, in the form of acomputer program comprising instructions executable by one or moreprocessors. The computer-readable storage medium is non-transitory. Insome embodiments, one or more (or all) of the aspects are performedusing software, etc. described in the other figures.

As a part of a configuration procedure, in some embodiments, a visualoutput from a video or still image source is displayed at 601. Forexample, an image depicting a scene is output. This visual output allowsfor a user to determine what sub-regions to focus on.

An indication of at least one region of the visual output to be analyzedis received at 603. In some embodiments, a user draws a shape (such as abox) surrounding each sub-region of interest. This shape is converted inco-ordinates in the image (and subsequent images) for cropping orextraction. In other embodiments, a user provides boundary co-ordinatesof the directly. Note that any shape defining a region may be utilizedsuch as rectangles, circles, etc. In some embodiments, additional imagemanipulation configuration is received. For example, what, if any,pre-processing of the sub-region should be performed and by what entity.Pre-processing may include, but is not limited to: color enhancement,up-sampling, down-sampling, increasing contrast, etc.

Per indicated sub-region, an indication of what ML model(s) to use toanalyze the indicated region is received at 605. The ML model(s) usageand image manipulation to obtain the at least one sub-region isconfigured at 606. In some embodiments, the image processing coordinator107 causes the display of 601, receives the indications of 603 and 605,and configures accordingly.

After the ML model(s) usage and image manipulation entities areconfigured, an image to analyze is received at 607. In some embodiments,this analysis includes cropping the at least one sub-region from thesubsequent image at 609. In some embodiments, pre-processing such as oneor more of performing color enhancement, adjusting contrast,up-sampling, down-sampling, etc. is performed on the at least onesub-region at 610. The indicated ML model(s) are applied to thesub-region (or pre-processed sub-region if pre-processing performed) at611.

In other embodiments, this analysis includes extracting the at least onesub-region from the subsequent image at 612. In some embodiments,pre-processing such as one or more of performing color enhancement,adjusting contrast, up-sampling, down-sampling, etc. is performed on theat least one sub-region at 613. The indicated ML model(s) are applied tothe sub-region (or pre-processed sub-region if pre-processing performed)at 614.

A result of the analyzing using the indicated ML model(s) is output at616. At some point later in time, a subsequent image will be received at607 and analyzed as detailed above.

In some embodiments, one or more actions are performed depending on theresult at 618. For example, the recipient may trigger an alert topersons in a building that wild animals are present on the property andto not use the picnic area, etc.

FIG. 7 is a block diagram of an illustrative operating environment inwhich machine learning models are trained and hosted according to someembodiments. The operating environment includes end user devices 702(for example, computing devices(s) 121, edge device(s) 141), a modeltraining system 120, a model hosting system 140, a training data store760, a training metrics data store 765, a container data store 770, atraining model data store 775, and a model prediction data store 780.

In some embodiments, users, by way of user devices 702, interact withthe model training system 120 to provide data that causes the modeltraining system 120 to train one or more machine learning models, forexample, as described elsewhere herein. A machine learning model,generally, may be thought of as one or more equations that are “trained”using a set of data. In some embodiments, the model training system 120provides ML functionalities as a web service, and thus messaging betweenuser devices 702 and the model training system 120 (or provider network100), and/or between components of the model training system 120 (orprovider network 100), can use HTTP messages to transfer data in amachine-readable file format, such as eXtensible Markup Language (XML)or JavaScript Object Notation (JSON). In some embodiments, providingaccess to various functionality as a web service is not limited tocommunications exchanged via the World Wide Web and more generallyrefers to a service that can communicate with other electronic devicesvia a computer network.

The user devices 702 can interact with the model training system 120 viafrontend 729 of the model training system 120. For example, a userdevice 702 can provide a training request to the frontend 729 thatincludes a container image (or multiple container images, or anidentifier of one or multiple locations where container images arestored), an indicator of input data (for example, an address or locationof input data), one or more hyperparameter values (for example, valuesindicating how the algorithm will operate, how many algorithms to run inparallel, how many clusters into which to separate data, and so forth),and/or information describing the computing machine on which to train amachine learning model (for example, a graphical processing unit (GPU)instance type, a central processing unit (CPU) instance type, an amountof memory to allocate, a type of virtual machine instance to use fortraining, and so forth).

In some embodiments, the container image can include one or more layers,where each layer represents an executable instruction. Some or all ofthe executable instructions together represent an algorithm that definesa machine learning model. The executable instructions (for example, thealgorithm) can be written in any programming language (for example,Python, Ruby, C++, Java, etc.). In some embodiments, the algorithm ispre-generated and obtained by a user, via the user device 702, from analgorithm repository (for example, a network-accessible marketplace, adata store provided by a machine learning training service, etc.). Insome embodiments, the algorithm is completely user-generated orpartially user-generated (for example, user-provided code modifies orconfigures existing algorithmic code).

In some embodiments, instead of providing a container image (oridentifier thereof) in the training request, the user device 702 mayprovide, in the training request, an algorithm written in anyprogramming language. The model training system 120 then packages thealgorithm into a container (optionally with other code, such as a “base”ML algorithm supplemented with user-provided code) that is eventuallyloaded into a virtual machine instance 722 for training a machinelearning model, as described in greater detail below. For example, auser, via a user device 702, may develop an algorithm/code using anapplication (for example, an interactive web-based programmingenvironment) and cause the algorithm/code to be provided—perhaps as partof a training request (or referenced in a training request)—to the modeltraining system 120, where this algorithm/code may be containerized onits own or used together with an existing container having a machinelearning framework, for example.

In some embodiments, instead of providing a container image in thetraining request, the user device 702 provides, in the training request,an indicator of a container image (for example, an indication of anaddress or a location at which a container image is stored). Forexample, the container image can be stored in a container data store770, and this container image may have been previously created/uploadedby the user. The model training system 120 can retrieve the containerimage from the indicated location and create a container using theretrieved container image. The container is then loaded into a virtualmachine instance 722 for training a machine learning model, as describedin greater detail below.

The model training system 120 can use the information provided by theuser device 702 to train a machine learning model in one or morepre-established virtual machine instances 722 in some embodiments. Inparticular, the model training system 120 includes a single physicalcomputing device or multiple physical computing devices that areinterconnected using one or more computing networks (not shown), wherethe physical computing device(s) host one or more virtual machineinstances 722. The model training system 120 can handle the acquisitionand configuration of compute capacity (for example, containers,instances, etc., which are described in greater detail below) based onthe information describing the computing machine on which to train amachine learning model provided by the user device 702. The modeltraining system 120 can then train machine learning models using thecompute capacity, as is described in greater detail below. The modeltraining system 120 can automatically scale up and down based on thevolume of training requests received from user devices 702 via frontend729, thereby relieving the user from the burden of having to worry aboutover-utilization (for example, acquiring too little computing resourcesand suffering performance issues) or under-utilization (for example,acquiring more computing resources than necessary to train the machinelearning models, and thus overpaying).

In some embodiments, the virtual machine instances 722 are utilized toexecute tasks. For example, such tasks can include training a machinelearning model. As shown in FIG. 7, each virtual machine instance 722includes an operating system (OS) 724, a language runtime 726, and oneor more ML training containers 730. Generally, the ML trainingcontainers 730 are logical units created within a virtual machineinstance using the resources available on that instance and can beutilized to isolate execution of a task from other processes (forexample, task executions) occurring in the instance. In someembodiments, the ML training containers 730 are formed from one or morecontainer images and a top container layer. Each container image mayfurther include one or more image layers, where each image layerrepresents an executable instruction. As described above, some or all ofthe executable instructions together represent an algorithm that definesa machine learning model. Changes made to the ML training containers 730(for example, creation of new files, modification of existing files,deletion of files, etc.) are stored in the top container layer. If a MLtraining container 730 is deleted, the top container layer is alsodeleted. However, the container image(s) that form a portion of thedeleted ML training container 730 can remain unchanged. The ML trainingcontainers 730 can be implemented, for example, as Linux containers(LXC), Docker containers, and the like.

The ML training containers 730 may include individual a runtime 734,code 737, and dependencies 732 needed by the code 737 in someembodiments. The runtime 734 can be defined by one or more executableinstructions that form at least a portion of a container image that isused to form the ML training container 730 (for example, the executableinstruction(s) in the container image that define the operating systemand/or runtime to run in the container formed from the container image).The code 737 includes one or more executable instructions that form atleast a portion of a container image that is used to form the MLtraining container 730. For example, the code 737 includes theexecutable instructions in the container image that represent analgorithm that defines a machine learning model, which may reference (orutilize) code or libraries from dependencies 732. The runtime 734 isconfigured to execute the code 737 in response to an instruction tobegin machine learning model training. Execution of the code 737 resultsin the generation of model data, as described in greater detail below.

In some embodiments, the code 737 includes executable instructions thatrepresent algorithms that define different machine learning models. Forexample, the code 737 includes one set of executable instructions thatrepresent a first algorithm that defines a first machine learning modeland a second set of executable instructions that represent a secondalgorithm that defines a second machine learning model. In someembodiments, the virtual machine instance 722 executes the code 737 andtrains all of the machine learning models. In some embodiments, thevirtual machine instance 722 executes the code 737, selecting one of themachine learning models to train. For example, the virtual machineinstance 722 can identify a type of training data indicated by thetraining request and select a machine learning model to train (forexample, execute the executable instructions that represent an algorithmthat defines the selected machine learning model) that corresponds withthe identified type of training data.

In some embodiments, the runtime 734 is the same as the runtime 726utilized by the virtual machine instance 722. In some embodiments, theruntime 734 is different than the runtime 726 utilized by the virtualmachine instance 722.

In some embodiments, the model training system 120 uses one or morecontainer images included in a training request (or a container imageretrieved from the container data store 770 in response to a receivedtraining request) to create and initialize a ML training container 730in a virtual machine instance 722. For example, the model trainingsystem 120 creates a ML training container 730 that includes thecontainer image(s) and/or a top container layer.

Prior to beginning the training process, in some embodiments, the modeltraining system 120 retrieves training data from the location indicatedin the training request. For example, the location indicated in thetraining request can be a location in the training data store 760. Thus,the model training system 120 retrieves the training data from theindicated location in the training data store 760. In some embodiments,the model training system 120 does not retrieve the training data priorto beginning the training process. Rather, the model training system 120streams the training data from the indicated location during thetraining process. For example, the model training system 120 caninitially retrieve a portion of the training data and provide theretrieved portion to the virtual machine instance 722 training themachine learning model. Once the virtual machine instance 722 hasapplied and used the retrieved portion or once the virtual machineinstance 722 is about to use all of the retrieved portion (for example,a buffer storing the retrieved portion is nearly empty), then the modeltraining system 120 can retrieve a second portion of the training dataand provide the second retrieved portion to the virtual machine instance722, and so on.

To perform the machine learning model training, the virtual machineinstance 722 executes code 737 stored in the ML training container 730in some embodiments. For example, the code 737 includes some or all ofthe executable instructions that form the container image of the MLtraining container 730 initialized therein. Thus, the virtual machineinstance 722 executes some or all of the executable instructions thatform the container image of the ML training container 730 initializedtherein to train a machine learning model. The virtual machine instance722 executes some or all of the executable instructions according to thehyperparameter values included in the training request. As anillustrative example, the virtual machine instance 722 trains a machinelearning model by identifying values for certain parameters (forexample, coefficients, weights, centroids, etc.). The identified valuesdepend on hyperparameters that define how the training is performed.Thus, the virtual machine instance 722 can execute the executableinstructions to initiate a machine learning model training process,where the training process is run using the hyperparameter valuesincluded in the training request. Execution of the executableinstructions can include the virtual machine instance 722 applying thetraining data retrieved by the model training system 120 as inputparameters to some or all of the instructions being executed.

In some embodiments, executing the executable instructions causes thevirtual machine instance 722 (for example, the ML training container730) to generate model data. For example, the ML training container 730generates model data and stores the model data in a file system of theML training container 730. The model data includes characteristics ofthe machine learning model being trained, such as a number of layers inthe machine learning model, hyperparameters of the machine learningmodel, coefficients of the machine learning model, weights of themachine learning model, and/or the like. In particular, the generatedmodel data includes values for the characteristics that define a machinelearning model being trained. In some embodiments, executing theexecutable instructions causes a modification to the ML trainingcontainer 730 such that the model data is written to the top containerlayer of the ML training container 730 and/or the container image(s)that forms a portion of the ML training container 730 is modified toinclude the model data.

The virtual machine instance 722 (or the model training system 120itself) pulls the generated model data from the ML training container730 and stores the generated model data in the training model data store775 in an entry associated with the virtual machine instance 722 and/orthe machine learning model being trained. In some embodiments, thevirtual machine instance 722 generates a single file that includes modeldata and stores the single file in the training model data store 775. Insome embodiments, the virtual machine instance 722 generates multiplefiles during the course of training a machine learning model, where eachfile includes model data. In some embodiments, each model data fileincludes the same or different model data information (for example, onefile identifies the structure of an algorithm, another file includes alist of coefficients, etc.). The virtual machine instance 722 canpackage the multiple files into a single file once training is completeand store the single file in the training model data store 775.Alternatively, the virtual machine instance 722 stores the multiplefiles in the training model data store 775. The virtual machine instance722 stores the file(s) in the training model data store 775 while thetraining process is ongoing and/or after the training process iscomplete.

In some embodiments, the virtual machine instance 722 regularly storesmodel data file(s) in the training model data store 775 as the trainingprocess is ongoing. Thus, model data file(s) can be stored in thetraining model data store 775 at different times during the trainingprocess. Each set of model data files corresponding to a particular timeor each set of model data files present in the training model data store775 as of a particular time could be checkpoints that representdifferent versions of a partially-trained machine learning model duringdifferent stages of the training process. Accordingly, before trainingis complete, a user, via the user device 702 can submit a deploymentand/or execution request in a manner as described below to deploy and/orexecute a version of a partially trained machine learning model (forexample, a machine learning model trained as of a certain stage in thetraining process). A version of a partially trained machine learningmodel can be based on some or all of the model data files stored in thetraining model data store 775.

In some embodiments, a virtual machine instance 722 executes code 737stored in a plurality of ML training containers 730. For example, thealgorithm included in the container image can be in a format that allowsfor the parallelization of the training process. Thus, the modeltraining system 120 can create multiple copies of the container imageprovided in a training request and cause the virtual machine instance722 to load each container image copy in a separate ML trainingcontainer 730. The virtual machine instance 722 can then execute, inparallel, the code 737 stored in the ML training containers 730. Thevirtual machine instance 722 can further provide configurationinformation to each ML training container 730 (for example, informationindicating that N ML training containers 730 are collectively training amachine learning model and that a particular ML training container 730receiving the configuration information is ML training container 730number X of N), which can be included in the resulting model data. Byparallelizing the training process, the model training system 120 cansignificantly reduce the training time in some embodiments.

In some embodiments, a plurality of virtual machine instances 722execute code 737 stored in a plurality of ML training containers 730.For example, the resources used to train a particular machine learningmodel can exceed the limitations of a single virtual machine instance722. However, the algorithm included in the container image can be in aformat that allows for the parallelization of the training process.Thus, the model training system 120 can create multiple copies of thecontainer image provided in a training request, initialize multiplevirtual machine instances 722, and cause each virtual machine instance722 to load a container image copy in one or more separate ML trainingcontainers 730. The virtual machine instances 722 can then each executethe code 737 stored in the ML training containers 730 in parallel. Themodel training system 120 can further provide configuration informationto each ML training container 730 via the virtual machine instances 722(for example, information indicating that N ML training containers 730are collectively training a machine learning model and that a particularML training container 730 receiving the configuration information is MLtraining container 730 number X of N, information indicating that Mvirtual machine instances 722 are collectively training a machinelearning model and that a particular ML training container 730 receivingthe configuration information is initialized in virtual machine instance722 number Y of M, etc.), which can be included in the resulting modeldata. As described above, by parallelizing the training process, themodel training system 120 can significantly reduce the training time insome embodiments.

In some embodiments, the model training system 120 includes a pluralityof physical computing devices and two or more of the physical computingdevices hosts one or more virtual machine instances 722 that execute thecode 737. Thus, the parallelization can occur over different physicalcomputing devices in addition to over different virtual machineinstances 722 and/or ML training containers 730.

In some embodiments, the model training system 120 includes a ML modelevaluator 728. The ML model evaluator 728 can monitor virtual machineinstances 722 as machine learning models are being trained, obtainingthe generated model data and processing the obtained model data togenerate model metrics. For example, the model metrics can includequality metrics, such as an error rate of the machine learning modelbeing trained, a statistical distribution of the machine learning modelbeing trained, a latency of the machine learning model being trained, aconfidence level of the machine learning model being trained (forexample, a level of confidence that the accuracy of the machine learningmodel being trained is known, etc. The ML model evaluator 728 can obtainthe model data for a machine learning model being trained and evaluationdata from the training data store 760. The evaluation data is separatefrom the data used to train a machine learning model and includes bothinput data and expected outputs (for example, known results), and thusthe ML model evaluator 728 can define a machine learning model using themodel data and execute the machine learning model by providing the inputdata as inputs to the machine learning model. The ML model evaluator 728can then compare the outputs of the machine learning model to theexpected outputs and determine one or more quality metrics of themachine learning model being trained based on the comparison (forexample, the error rate can be a difference or distance between themachine learning model outputs and the expected outputs).

The ML model evaluator 728 periodically generates model metrics duringthe training process and stores the model metrics in the trainingmetrics data store 765 in some embodiments. While the machine learningmodel is being trained, a user, via the user device 702, can access andretrieve the model metrics from the training metrics data store 765. Theuser can then use the model metrics to determine whether to adjust thetraining process and/or to stop the training process. For example, themodel metrics can indicate that the machine learning model is performingpoorly (for example, has an error rate above a threshold value, has astatistical distribution that is not an expected or desired distribution(for example, not a binomial distribution, a Poisson distribution, ageometric distribution, a normal distribution, Gaussian distribution,etc.), has an execution latency above a threshold value, has aconfidence level below a threshold value)) and/or is performingprogressively worse (for example, the quality metric continues to worsenover time). In response, in some embodiments, the user, via the userdevice 702, can transmit a request to the model training system 120 tomodify the machine learning model being trained (for example, transmit amodification request). The request can include a new or modifiedcontainer image, a new or modified algorithm, new or modifiedhyperparameter(s), and/or new or modified information describing thecomputing machine on which to train a machine learning model. The modeltraining system 120 can modify the machine learning model accordingly.For example, the model training system 120 can cause the virtual machineinstance 722 to optionally delete an existing ML training container 730,create and initialize a new ML training container 730 using some or allof the information included in the request, and execute the code 737stored in the new ML training container 730 to restart the machinelearning model training process. As another example, the model trainingsystem 120 can cause the virtual machine instance 722 to modify theexecution of code stored in an existing ML training container 730according to the data provided in the modification request. In someembodiments, the user, via the user device 702, can transmit a requestto the model training system 120 to stop the machine learning modeltraining process. The model training system 120 can then instruct thevirtual machine instance 722 to delete the ML training container 730and/or to delete any model data stored in the training model data store775.

As described below, in some embodiments, the model data stored in thetraining model data store 775 is used by the model hosting system 140 todeploy machine learning models. Alternatively, or additionally, a userdevice 702 or another computing device (not shown) can retrieve themodel data from the training model data store 775 to implement alearning algorithm in an external device. As an illustrative example, arobotic device can include sensors to capture input data. A user device702 can retrieve the model data from the training model data store 775and store the model data in the robotic device. The model data defines amachine learning model. Thus, the robotic device can provide thecaptured input data as an input to the machine learning model, resultingin an output. The robotic device can then perform an action (forexample, move forward, raise an arm, generate a sound, etc.) based onthe resulting output.

While the virtual machine instances 722 are shown in FIG. 7 as a singlegrouping of virtual machine instances 722, some embodiments of thepresent application separate virtual machine instances 722 that areactively assigned to execute tasks from those virtual machine instances722 that are not actively assigned to execute tasks. For example, thosevirtual machine instances 722 actively assigned to execute tasks aregrouped into an “active pool,” while those virtual machine instances 722not actively assigned to execute tasks are placed within a “warmingpool.” In some embodiments, those virtual machine instances 722 withinthe warming pool can be pre-initialized with an operating system,language runtimes, and/or other software required to enable rapidexecution of tasks (for example, rapid initialization of machinelearning model training in ML training container(s) 730) in response totraining requests.

In some embodiments, the model training system 120 includes a processingunit, a network interface, a computer-readable medium drive, and aninput/output device interface, all of which can communicate with oneanother by way of a communication bus. The network interface can provideconnectivity to one or more networks or computing systems. Theprocessing unit can thus receive information and instructions from othercomputing systems or services (for example, user devices 702, the modelhosting system 140, etc.). The processing unit can also communicate toand from a memory of a virtual machine instance 722 and further provideoutput information for an optional display via the input/output deviceinterface. The input/output device interface can also accept input froman optional input device. The memory can contain computer programinstructions (grouped as modules in some embodiments) that theprocessing unit executes in order to implement one or more aspects ofthe present disclosure.

In some embodiments, the model hosting system 140 includes a singlephysical computing device or multiple physical computing devices thatare interconnected using one or more computing networks (not shown),where the physical computing device(s) host one or more virtual machineinstances 742. The model hosting system 140 can handle the acquisitionand configuration of compute capacity (for example, containers,instances, etc.) based on demand for the execution of trained machinelearning models. The model hosting system 140 can then execute machinelearning models using the compute capacity, as is described in greaterdetail below. The model hosting system 140 can automatically scale upand down based on the volume of execution requests received from userdevices 702 via frontend 749 of the model hosting system 140, therebyrelieving the user from the burden of having to worry aboutover-utilization (for example, acquiring too little computing resourcesand suffering performance issues) or under-utilization (for example,acquiring more computing resources than necessary to run the machinelearning models, and thus overpaying).

In some embodiments, the virtual machine instances 742 are utilized toexecute tasks. For example, such tasks can include executing a machinelearning model. As shown in FIG. 7, each virtual machine instance 742includes an operating system (OS) 744, a language runtime 746, and oneor more ML scoring containers 750. The ML scoring containers 750 aresimilar to the ML training containers 730 in that the ML scoringcontainers 750 are logical units created within a virtual machineinstance using the resources available on that instance and can beutilized to isolate execution of a task from other processes (forexample, task executions) occurring in the instance. In someembodiments, the ML scoring containers 750 are formed from one or morecontainer images and a top container layer. Each container image furtherincludes one or more image layers, where each image layer represents anexecutable instruction. As described above, some or all of theexecutable instructions together represent an algorithm that defines amachine learning model. Changes made to the ML scoring containers 750(for example, creation of new files, modification of existing files,deletion of files, etc.) are stored in the top container layer. If a MLscoring container 750 is deleted, the top container layer is alsodeleted. However, the container image(s) that form a portion of thedeleted ML scoring container 750 can remain unchanged. The ML scoringcontainers 750 can be implemented, for example, as Linux containers.

The ML scoring containers 750 each include a runtime 754, code 756, anddependencies 752 (for example, supporting software such as libraries)needed by the code 756 in some embodiments. The runtime 754 can bedefined by one or more executable instructions that form at least aportion of a container image that is used to form the ML scoringcontainer 750 (for example, the executable instruction(s) in thecontainer image that define the operating system and/or runtime to runin the container formed from the container image). The code 756 includesone or more executable instructions that form at least a portion of acontainer image that is used to form the ML scoring container 750. Forexample, the code 756 includes the executable instructions in thecontainer image that represent an algorithm that defines a machinelearning model, which may reference dependencies 752. The code 756 canalso include model data that represent characteristics of the definedmachine learning model, as described in greater detail below. Theruntime 754 is configured to execute the code 756 in response to aninstruction to begin execution of a machine learning model. Execution ofthe code 756 results in the generation of outputs (for example,predicted results), as described in greater detail below.

In some embodiments, the runtime 754 is the same as the runtime 746utilized by the virtual machine instance 742. In some embodiments,runtime 754 is different than the runtime 746 utilized by the virtualmachine instance 742.

In some embodiments, the model hosting system 140 uses one or morecontainer images included in a deployment request (or a container imageretrieved from the container data store 770 in response to a receiveddeployment request) to create and initialize a ML scoring container 750in a virtual machine instance 742. For example, the model hosting system140 creates a ML scoring container 750 that includes the containerimage(s) and/or a top container layer.

As described above, a user device 702 can submit a deployment requestand/or an execution request to the model hosting system 140 via thefrontend 749 in some embodiments. A deployment request causes the modelhosting system 140 to deploy a trained machine learning model into avirtual machine instance 742. For example, the deployment request caninclude an identification of an endpoint (for example, an endpoint name,such as an HTTP endpoint name) and an identification of one or moretrained machine learning models (for example, a location of one or moremodel data files stored in the training model data store 775).Optionally, the deployment request also includes an identification ofone or more container images stored in the container data store 770.

Upon receiving the deployment request, the model hosting system 140initializes ones or more ML scoring containers 750 in one or more hostedvirtual machine instance 742. In embodiments in which the deploymentrequest includes an identification of one or more container images, themodel hosting system 140 forms the ML scoring container(s) 750 from theidentified container image(s). For example, a container image identifiedin a deployment request can be the same container image used to form anML training container 730 used to train the machine learning modelcorresponding to the deployment request. Thus, the code 756 of the MLscoring container(s) 750 includes one or more executable instructions inthe container image(s) that represent an algorithm that defines amachine learning model. In embodiments in which the deployment requestdoes not include an identification of a container image, the modelhosting system 140 forms the ML scoring container(s) 750 from one ormore container images stored in the container data store 770 that areappropriate for executing the identified trained machine learningmodel(s). For example, an appropriate container image can be a containerimage that includes executable instructions that represent an algorithmthat defines the identified trained machine learning model(s).

The model hosting system 140 further forms the ML scoring container(s)750 by retrieving model data corresponding to the identified trainedmachine learning model(s) in some embodiments. For example, thedeployment request can identify a location of model data file(s) storedin the training model data store 775. In embodiments in which a singlemodel data file is identified in the deployment request, the modelhosting system 140 retrieves the identified model data file from thetraining model data store 775 and inserts the model data file into asingle ML scoring container 750, which forms a portion of code 756. Insome embodiments, the model data file is archived or compressed (forexample, formed from a package of individual files). Thus, the modelhosting system 140 unarchives or decompresses the model data file toobtain multiple individual files and inserts the individual files intothe ML scoring container 750. In some embodiments, the model hostingsystem 140 stores the model data file in the same location as thelocation in which the model data file was stored in the ML trainingcontainer 730 that generated the model data file. For example, the modeldata file initially was stored in the top container layer of the MLtraining container 730 at a certain offset, and the model hosting system140 then stores the model data file in the top container layer of the MLscoring container 750 at the same offset.

In embodiments in which multiple model data files are identified in thedeployment request, the model hosting system 140 retrieves theidentified model data files from the training model data store 775. Themodel hosting system 140 can insert the model data files into the sameML scoring container 750, into different ML scoring containers 750initialized in the same virtual machine instance 742, or into differentML scoring containers 750 initialized in different virtual machineinstances 742. As an illustrative example, the deployment request canidentify multiple model data files corresponding to different trainedmachine learning models because the trained machine learning models arerelated (for example, the output of one trained machine learning modelis used as an input to another trained machine learning model). Thus,the user may desire to deploy multiple machine learning models toeventually receive a single output that relies on the outputs ofmultiple machine learning models.

In some embodiments, the model hosting system 140 associates theinitialized ML scoring container(s) 750 with the endpoint identified inthe deployment request. For example, each of the initialized ML scoringcontainer(s) 750 can be associated with a network address. The modelhosting system 140 can map the network address(es) to the identifiedendpoint, and the model hosting system 140 or another system (forexample, a routing system, not shown) can store the mapping. Thus, auser device 702 can refer to trained machine learning model(s) stored inthe ML scoring container(s) 750 using the endpoint. This allows for thenetwork address of an ML scoring container 750 to change without causingthe user operating the user device 702 to change the way in which theuser refers to a trained machine learning model.

Once the ML scoring container(s) 750 are initialized, the ML scoringcontainer(s) 750 are ready to execute trained machine learning model(s).In some embodiments, the user device 702 transmits an execution requestto the model hosting system 140 via the frontend 749, where theexecution request identifies an endpoint and includes an input to amachine learning model (for example, a set of input data). The modelhosting system 140 or another system (for example, a routing system, notshown) can obtain the execution request, identify the ML scoringcontainer(s) 750 corresponding to the identified endpoint, and route theinput to the identified ML scoring container(s) 750.

In some embodiments, a virtual machine instance 742 executes the code756 stored in an identified ML scoring container 750 in response to themodel hosting system 140 receiving the execution request. In particular,execution of the code 756 causes the executable instructions in the code756 corresponding to the algorithm to read the model data file stored inthe ML scoring container 750, use the input included in the executionrequest as an input parameter, and generate a corresponding output. Asan illustrative example, the algorithm can include coefficients,weights, layers, cluster centroids, and/or the like. The executableinstructions in the code 756 corresponding to the algorithm can read themodel data file to determine values for the coefficients, weights,layers, cluster centroids, and/or the like. The executable instructionscan include input parameters, and the input included in the executionrequest can be supplied by the virtual machine instance 742 as the inputparameters. With the machine learning model characteristics and theinput parameters provided, execution of the executable instructions bythe virtual machine instance 742 can be completed, resulting in anoutput.

In some embodiments, the virtual machine instance 742 stores the outputin the model prediction data store 780. Alternatively, or in addition,the virtual machine instance 742 transmits the output to the user device702 that submitted the execution result via the frontend 749.

In some embodiments, the execution request corresponds to a group ofrelated trained machine learning models. Thus, the ML scoring container750 can transmit the output to a second ML scoring container 750initialized in the same virtual machine instance 742 or in a differentvirtual machine instance 742. The virtual machine instance 742 thatinitialized the second ML scoring container 750 can then execute secondcode 756 stored in the second ML scoring container 750, providing thereceived output as an input parameter to the executable instructions inthe second code 756. The second ML scoring container 750 furtherincludes a model data file stored therein, which is read by theexecutable instructions in the second code 756 to determine values forthe characteristics defining the machine learning model. Execution ofthe second code 756 results in a second output. The virtual machineinstance 742 that initialized the second ML scoring container 750 canthen transmit the second output to the model prediction data store 780and/or the user device 702 via the frontend 749 (for example, if no moretrained machine learning models are needed to generate an output) ortransmit the second output to a third ML scoring container 750initialized in the same or different virtual machine instance 742 (forexample, if outputs from one or more additional trained machine learningmodels are needed), and the above-referenced process can be repeatedwith respect to the third ML scoring container 750.

While the virtual machine instances 742 are shown in FIG. 7 as a singlegrouping of virtual machine instances 742, some embodiments of thepresent application separate virtual machine instances 742 that areactively assigned to execute tasks from those virtual machine instances742 that are not actively assigned to execute tasks. For example, thosevirtual machine instances 742 actively assigned to execute tasks aregrouped into an “active pool,” while those virtual machine instances 742not actively assigned to execute tasks are placed within a “warmingpool.” In some embodiments, those virtual machine instances 742 withinthe warming pool can be pre-initialized with an operating system,language runtimes, and/or other software required to enable rapidexecution of tasks (for example, rapid initialization of ML scoringcontainer(s) 750, rapid execution of code 756 in ML scoringcontainer(s), etc.) in response to deployment and/or execution requests.

In some embodiments, the model hosting system 140 includes a processingunit, a network interface, a computer-readable medium drive, and aninput/output device interface, all of which can communicate with oneanother by way of a communication bus. The network interface can provideconnectivity to one or more networks or computing systems. Theprocessing unit can thus receive information and instructions from othercomputing systems or services (for example, user devices 702, the modeltraining system 120, etc.). The processing unit can also communicate toand from a memory of a virtual machine instance 742 and further provideoutput information for an optional display via the input/output deviceinterface. The input/output device interface can also accept input froman optional input device. The memory can contain computer programinstructions (grouped as modules in some embodiments) that theprocessing unit executes in order to implement one or more aspects ofthe present disclosure.

In some embodiments, the operating environment supports many differenttypes of machine learning models, such as multi arm bandit models,reinforcement learning models, ensemble machine learning models, deeplearning models, and/or the like.

The model training system 120 and the model hosting system 140 depictedin FIG. 7 are not meant to be limiting. For example, the model trainingsystem 120 and/or the model hosting system 140 could also operate withina computing environment having a fewer or greater number of devices thanare illustrated in FIG. 7. Thus, the depiction of the model trainingsystem 120 and/or the model hosting system 140 in FIG. 7 may be taken asillustrative and not limiting to the present disclosure. For example,the model training system 120 and/or the model hosting system 140 orvarious constituents thereof could implement various web servicescomponents, hosted or “cloud” computing environments, and/orpeer-to-peer network configurations to implement at least a portion ofthe processes described herein. In some embodiments, the model trainingsystem 120 and/or the model hosting system 140 are implemented directlyin hardware or software executed by hardware devices and may, forinstance, include one or more physical or virtual servers implemented onphysical computer hardware configured to execute computer-executableinstructions for performing the various features that are describedherein. The one or more servers can be geographically dispersed orgeographically co-located, for instance, in one or more points ofpresence (POPs) or regional data centers.

The frontend 729 processes all training requests received from userdevices 702 and provisions virtual machine instances 722. In someembodiments, the frontend 729 serves as a front door to all the otherservices provided by the model training system 120. The frontend 729processes the requests and makes sure that the requests are properlyauthorized. For example, the frontend 729 may determine whether the userassociated with the training request is authorized to initiate thetraining process.

Similarly, frontend 749 processes all deployment and execution requestsreceived from user devices 702 and provisions virtual machine instances742. In some embodiments, the frontend 749 serves as a front door to allthe other services provided by the model hosting system 140. Thefrontend 749 processes the requests and makes sure that the requests areproperly authorized. For example, the frontend 749 may determine whetherthe user associated with a deployment request or an execution request isauthorized to access the indicated model data and/or to execute theindicated machine learning model.

The training data store 760 stores training data and/or evaluation data.The training data can be data used to train machine learning models andevaluation data can be data used to evaluate the performance of machinelearning models. In some embodiments, the training data and theevaluation data have common data. In some embodiments, the training dataand the evaluation data do not have common data. In some embodiments,the training data includes input data and expected outputs. While thetraining data store 760 is depicted as being located external to themodel training system 120 and the model hosting system 140, this is notmeant to be limiting. For example, in some embodiments not shown, thetraining data store 760 is located internal to at least one of the modeltraining system 120 or the model hosting system 140.

In some embodiments, the training metrics data store 765 stores modelmetrics. While the training metrics data store 765 is depicted as beinglocated external to the model training system 120 and the model hostingsystem 140, this is not meant to be limiting. For example, in someembodiments not shown, the training metrics data store 765 is locatedinternal to at least one of the model training system 120 or the modelhosting system 140.

The container data store 770 stores container images, such as containerimages used to form ML training containers 730 and/or ML scoringcontainers 750, that can be retrieved by various virtual machineinstances 722 and/or 742. While the container data store 770 is depictedas being located external to the model training system 120 and the modelhosting system 140, this is not meant to be limiting. For example, insome embodiments not shown, the container data store 770 is locatedinternal to at least one of the model training system 120 and the modelhosting system 140.

The training model data store 775 stores model data files. In someembodiments, some of the model data files are comprised of a singlefile, while other model data files are packages of multiple individualfiles. While the training model data store 775 is depicted as beinglocated external to the model training system 120 and the model hostingsystem 140, this is not meant to be limiting. For example, in someembodiments not shown, the training model data store 775 is locatedinternal to at least one of the model training system 120 or the modelhosting system 140.

The model prediction data store 780 stores outputs (for example,execution results) generated by the ML scoring containers 750 in someembodiments. While the model prediction data store 780 is depicted asbeing located external to the model training system 120 and the modelhosting system 140, this is not meant to be limiting. For example, insome embodiments not shown, the model prediction data store 780 islocated internal to at least one of the model training system 120 andthe model hosting system 140.

While the model training system 120, the model hosting system 140, thetraining data store 760, the training metrics data store 765, thecontainer data store 770, the training model data store 775, and themodel prediction data store 780 are illustrated as separate components,this is not meant to be limiting. In some embodiments, any one or all ofthese components can be combined to perform the functionality describedherein. For example, any one or all of these components can beimplemented by a single computing device, or by multiple distinctcomputing devices, such as computer servers, logically or physicallygrouped together to collectively operate as a server system. Any one orall of these components can communicate via a shared internal network,and the collective system (for example, also referred to herein as amachine learning service) can communicate with one or more of the userdevices 702 via the one or more network(s) 106.

Various example user devices 702 are shown in FIG. 7, including adesktop computer, laptop, and a mobile phone, each provided by way ofillustration. In general, the user devices 702 can be any computingdevice such as a desktop, laptop or tablet computer, personal computer,wearable computer, server, personal digital assistant (PDA), hybridPDA/mobile phone, mobile phone, electronic book reader, set-top box,voice command device, camera, digital media player, and the like. Insome embodiments, the model training system 120 and/or the model hostingsystem 140 provides the user devices 702 with one or more userinterfaces, command-line interfaces (CLI), application programinginterfaces (API), and/or other programmatic interfaces for submittingtraining requests, deployment requests, and/or execution requests. Insome embodiments, the user devices 702 can execute a stand-aloneapplication that interacts with the model training system 120 and/or themodel hosting system 140 for submitting training requests, deploymentrequests, and/or execution requests.

In some embodiments, the network 106 includes any wired network,wireless network, or combination thereof. For example, the network 106may be a personal area network, local area network, wide area network,over-the-air broadcast network (for example, for radio or television),cable network, satellite network, cellular telephone network, orcombination thereof. As a further example, the network 106 may be apublicly accessible network of linked networks, possibly operated byvarious distinct parties, such as the Internet. In some embodiments, thenetwork 106 may be a private or semi-private network, such as acorporate or university intranet. The network 106 may include one ormore wireless networks, such as a Global System for MobileCommunications (GSM) network, a Code Division Multiple Access (CDMA)network, a Long Term Evolution (LTE) network, or any other type ofwireless network. The network 106 can use protocols and components forcommunicating via the Internet or any of the other aforementioned typesof networks. For example, the protocols used by the network 106 mayinclude HTTP, HTTP Secure (HTTPS), Message Queue Telemetry Transport(MQTT), Constrained Application Protocol (CoAP), and the like. Protocolsand components for communicating via the Internet or any of the otheraforementioned types of communication networks are well known to thoseskilled in the art and, thus, are not described in more detail herein.

FIG. 8 illustrates an example provider network (or “service providersystem”) environment according to some embodiments. A provider network800 may provide resource virtualization to customers via one or morevirtualization services 810 that allow customers to purchase, rent, orotherwise obtain instances 812 of virtualized resources, including butnot limited to computation and storage resources, implemented on deviceswithin the provider network or networks in one or more data centers.Local Internet Protocol (IP) addresses 816 may be associated with theresource instances 812; the local IP addresses are the internal networkaddresses of the resource instances 812 on the provider network 800. Insome embodiments, the provider network 800 may also provide public IPaddresses 814 and/or public IP address ranges (e.g., Internet Protocolversion 4 (IPv4) or Internet Protocol version 6 (IPv6) addresses) thatcustomers may obtain from the provider 800.

Conventionally, the provider network 800, via the virtualizationservices 810, may allow a customer of the service provider (e.g., acustomer that operates one or more client networks 850A-850C includingone or more customer device(s) 852) to dynamically associate at leastsome public IP addresses 814 assigned or allocated to the customer withparticular resource instances 812 assigned to the customer. The providernetwork 800 may also allow the customer to remap a public IP address814, previously mapped to one virtualized computing resource instance812 allocated to the customer, to another virtualized computing resourceinstance 812 that is also allocated to the customer. Using thevirtualized computing resource instances 812 and public IP addresses 814provided by the service provider, a customer of the service providersuch as the operator of customer network(s) 850A-850C may, for example,implement customer-specific applications and present the customer'sapplications on an intermediate network 840, such as the Internet. Othernetwork entities 820 on the intermediate network 840 may then generatetraffic to a destination public IP address 814 published by the customernetwork(s) 850A-850C; the traffic is routed to the service provider datacenter, and at the data center is routed, via a network substrate, tothe local IP address 816 of the virtualized computing resource instance812 currently mapped to the destination public IP address 814.Similarly, response traffic from the virtualized computing resourceinstance 812 may be routed via the network substrate back onto theintermediate network 840 to the source entity 820.

Local IP addresses, as used herein, refer to the internal or “private”network addresses, for example, of resource instances in a providernetwork. Local IP addresses can be within address blocks reserved byInternet Engineering Task Force (IETF) Request for Comments (RFC) 1918and/or of an address format specified by IETF RFC 4193 and may bemutable within the provider network. Network traffic originating outsidethe provider network is not directly routed to local IP addresses;instead, the traffic uses public IP addresses that are mapped to thelocal IP addresses of the resource instances. The provider network mayinclude networking devices or appliances that provide network addresstranslation (NAT) or similar functionality to perform the mapping frompublic IP addresses to local IP addresses and vice versa.

Public IP addresses are Internet mutable network addresses that areassigned to resource instances, either by the service provider or by thecustomer. Traffic routed to a public IP address is translated, forexample via 1:1 NAT, and forwarded to the respective local IP address ofa resource instance.

Some public IP addresses may be assigned by the provider networkinfrastructure to particular resource instances; these public IPaddresses may be referred to as standard public IP addresses, or simplystandard IP addresses. In some embodiments, the mapping of a standard IPaddress to a local IP address of a resource instance is the defaultlaunch configuration for all resource instance types.

At least some public IP addresses may be allocated to or obtained bycustomers of the provider network 800; a customer may then assign theirallocated public IP addresses to particular resource instances allocatedto the customer. These public IP addresses may be referred to ascustomer public IP addresses, or simply customer IP addresses. Insteadof being assigned by the provider network 800 to resource instances asin the case of standard IP addresses, customer IP addresses may beassigned to resource instances by the customers, for example via an APIprovided by the service provider. Unlike standard IP addresses, customerIP addresses are allocated to customer accounts and can be remapped toother resource instances by the respective customers as necessary ordesired. A customer IP address is associated with a customer's account,not a particular resource instance, and the customer controls that IPaddress until the customer chooses to release it. Unlike conventionalstatic IP addresses, customer IP addresses allow the customer to maskresource instance or availability zone failures by remapping thecustomer's public IP addresses to any resource instance associated withthe customer's account. The customer IP addresses, for example, enable acustomer to engineer around problems with the customer's resourceinstances or software by remapping customer IP addresses to replacementresource instances.

FIG. 9 is a block diagram of an example provider network that provides astorage service and a hardware virtualization service to customers,according to some embodiments. Hardware virtualization service 920provides multiple computation resources 924 (e.g., VMs) to customers.The computation resources 924 may, for example, be rented or leased tocustomers of the provider network 900 (e.g., to a customer thatimplements customer network 950). Each computation resource 924 may beprovided with one or more local IP addresses. Provider network 900 maybe configured to route packets from the local IP addresses of thecomputation resources 924 to public Internet destinations, and frompublic Internet sources to the local IP addresses of computationresources 924.

Provider network 900 may provide a customer network 950, for examplecoupled to intermediate network 940 via local network 956, the abilityto implement virtual computing systems 992 via hardware virtualizationservice 920 coupled to intermediate network 940 and to provider network900. In some embodiments, hardware virtualization service 920 mayprovide one or more APIs 902, for example a web services interface, viawhich a customer network 950 may access functionality provided by thehardware virtualization service 920, for example via a console 994(e.g., a web-based application, standalone application, mobileapplication, etc.). In some embodiments, at the provider network 900,each virtual computing system 992 at customer network 950 may correspondto a computation resource 924 that is leased, rented, or otherwiseprovided to customer network 950.

From an instance of a virtual computing system 992 and/or anothercustomer device 990 (e.g., via console 994), the customer may access thefunctionality of storage service 910, for example via one or more APIs902, to access data from and store data to storage resources 918A-918Nof a virtual data store 916 (e.g., a folder or “bucket”, a virtualizedvolume, a database, etc.) provided by the provider network 900. In someembodiments, a virtualized data store gateway (not shown) may beprovided at the customer network 950 that may locally cache at leastsome data, for example frequently-accessed or critical data, and thatmay communicate with storage service 910 via one or more communicationschannels to upload new or modified data from a local cache so that theprimary store of data (virtualized data store 916) is maintained. Insome embodiments, a user, via a virtual computing system 992 and/or onanother customer device 990, may mount and access virtual data store 916volumes via storage service 910 acting as a storage virtualizationservice, and these volumes may appear to the user as local (virtualized)storage 998.

While not shown in FIG. 9, the virtualization service(s) may also beaccessed from resource instances within the provider network 900 viaAPI(s) 902. For example, a customer, appliance service provider, orother entity may access a virtualization service from within arespective virtual network on the provider network 900 via an API 902 torequest allocation of one or more resource instances within the virtualnetwork or within another virtual network.

Illustrative Systems

In some embodiments, a system that implements a portion or all of thetechniques described herein may include a general-purpose computersystem that includes or is configured to access one or morecomputer-accessible media, such as computer system 1000 illustrated inFIG. 10. In the illustrated embodiment, computer system 1000 includesone or more processors 1010 coupled to a system memory 1020 via aninput/output (I/O) interface 1030. Computer system 1000 further includesa network interface 1040 coupled to I/O interface 1030. While FIG. 10shows computer system 1000 as a single computing device, in variousembodiments a computer system 1000 may include one computing device orany number of computing devices configured to work together as a singlecomputer system 1000.

In various embodiments, computer system 1000 may be a uniprocessorsystem including one processor 1010, or a multiprocessor systemincluding several processors 1010 (e.g., two, four, eight, or anothersuitable number). Processors 1010 may be any suitable processors capableof executing instructions. For example, in various embodiments,processors 1010 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, ARM, PowerPC, SPARC, or MIPS ISAs, or any othersuitable ISA. In multiprocessor systems, each of processors 1010 maycommonly, but not necessarily, implement the same ISA.

System memory 1020 may store instructions and data accessible byprocessor(s) 1010. In various embodiments, system memory 1020 may beimplemented using any suitable memory technology, such as random-accessmemory (RAM), static RAM (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory. In theillustrated embodiment, program instructions and data implementing oneor more desired functions, such as those methods, techniques, and datadescribed above are shown stored within system memory 1020 as code 1025and data 1026.

In one embodiment, I/O interface 1030 may be configured to coordinateI/O traffic between processor 1010, system memory 1020, and anyperipheral devices in the device, including network interface 1040 orother peripheral interfaces. In some embodiments, I/O interface 1030 mayperform any necessary protocol, timing or other data transformations toconvert data signals from one component (e.g., system memory 1020) intoa format suitable for use by another component (e.g., processor 1010).In some embodiments, I/O interface 1030 may include support for devicesattached through various types of peripheral buses, such as a variant ofthe Peripheral Component Interconnect (PCI) bus standard or theUniversal Serial Bus (USB) standard, for example. In some embodiments,the function of I/O interface 1030 may be split into two or moreseparate components, such as a north bridge and a south bridge, forexample. Also, in some embodiments some or all of the functionality ofI/O interface 1030, such as an interface to system memory 1020, may beincorporated directly into processor 1010.

Network interface 1040 may be configured to allow data to be exchangedbetween computer system 1000 and other devices 1060 attached to anetwork or networks 1050, such as other computer systems or devices asillustrated in FIG. 1, for example. In various embodiments, networkinterface 1040 may support communication via any suitable wired orwireless general data networks, such as types of Ethernet network, forexample. Additionally, network interface 1040 may support communicationvia telecommunications/telephony networks such as analog voice networksor digital fiber communications networks, via storage area networks(SANs) such as Fibre Channel SANs, or via I/O any other suitable type ofnetwork and/or protocol.

In some embodiments, a computer system 1000 includes one or more offloadcards 1070 (including one or more processors 1075, and possiblyincluding the one or more network interfaces 1040) that are connectedusing an I/O interface 1030 (e.g., a bus implementing a version of thePeripheral Component Interconnect-Express (PCI-E) standard, or anotherinterconnect such as a QuickPath interconnect (QPI) or UltraPathinterconnect (UPI)). For example, in some embodiments the computersystem 1000 may act as a host electronic device (e.g., operating as partof a hardware virtualization service) that hosts compute instances, andthe one or more offload cards 1070 execute a virtualization manager thatcan manage compute instances that execute on the host electronic device.As an example, in some embodiments the offload card(s) 1070 can performcompute instance management operations such as pausing and/or un-pausingcompute instances, launching and/or terminating compute instances,performing memory transfer/copying operations, etc. These managementoperations may, in some embodiments, be performed by the offload card(s)1070 in coordination with a hypervisor (e.g., upon a request from ahypervisor) that is executed by the other processors 1010A-1010N of thecomputer system 1000. However, in some embodiments the virtualizationmanager implemented by the offload card(s) 1070 can accommodate requestsfrom other entities (e.g., from compute instances themselves), and maynot coordinate with (or service) any separate hypervisor.

In some embodiments, system memory 1020 may be one embodiment of acomputer-accessible medium configured to store program instructions anddata as described above. However, in other embodiments, programinstructions and/or data may be received, sent or stored upon differenttypes of computer-accessible media. Generally speaking, acomputer-accessible medium may include non-transitory storage media ormemory media such as magnetic or optical media, e.g., disk or DVD/CDcoupled to computer system 1000 via I/O interface 1030. A non-transitorycomputer-accessible storage medium may also include any volatile ornon-volatile media such as RAM (e.g., SDRAM, double data rate (DDR)SDRAM, SRAM, etc.), read only memory (ROM), etc., that may be includedin some embodiments of computer system 1000 as system memory 1020 oranother type of memory. Further, a computer-accessible medium mayinclude transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network and/or a wireless link, such as may be implemented vianetwork interface 1040.

Various embodiments discussed or suggested herein can be implemented ina wide variety of operating environments, which in some cases caninclude one or more user computers, computing devices, or processingdevices which can be used to operate any of a number of applications.User or client devices can include any of a number of general-purposepersonal computers, such as desktop or laptop computers running astandard operating system, as well as cellular, wireless, and handhelddevices running mobile software and capable of supporting a number ofnetworking and messaging protocols. Such a system also can include anumber of workstations running any of a variety of commerciallyavailable operating systems and other known applications for purposessuch as development and database management. These devices also caninclude other electronic devices, such as dummy terminals, thin-clients,gaming systems, and/or other devices capable of communicating via anetwork.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of widely-available protocols, such as Transmission ControlProtocol/Internet Protocol (TCP/IP), File Transfer Protocol (FTP),Universal Plug and Play (UPnP), Network File System (NFS), CommonInternet File System (CIFS), Extensible Messaging and Presence Protocol(XMPP), AppleTalk, etc. The network(s) can include, for example, a localarea network (LAN), a wide-area network (WAN), a virtual private network(VPN), the Internet, an intranet, an extranet, a public switchedtelephone network (PSTN), an infrared network, a wireless network, andany combination thereof.

In embodiments utilizing a web server, the web server can run any of avariety of server or mid-tier applications, including HTTP servers, FileTransfer Protocol (FTP) servers, Common Gateway Interface (CGI) servers,data servers, Java servers, business application servers, etc. Theserver(s) also may be capable of executing programs or scripts inresponse requests from user devices, such as by executing one or moreWeb applications that may be implemented as one or more scripts orprograms written in any programming language, such as Java®, C, C# orC++, or any scripting language, such as Perl, Python, PHP, or TCL, aswell as combinations thereof. The server(s) may also include databaseservers, including without limitation those commercially available fromOracle®, Microsoft®, Sybase®, IBM®, etc. The database servers may berelational or non-relational (e.g., “NoSQL”), distributed ornon-distributed, etc.

Environments disclosed herein can include a variety of data stores andother memory and storage media as discussed above. These can reside in avariety of locations, such as on a storage medium local to (and/orresident in) one or more of the computers or remote from any or all ofthe computers across the network. In a particular set of embodiments,the information may reside in a storage-area network (SAN) familiar tothose skilled in the art. Similarly, any necessary files for performingthe functions attributed to the computers, servers, or other networkdevices may be stored locally and/or remotely, as appropriate. Where asystem includes computerized devices, each such device can includehardware elements that may be electrically coupled via a bus, theelements including, for example, at least one central processing unit(CPU), at least one input device (e.g., a mouse, keyboard, controller,touch screen, or keypad), and/or at least one output device (e.g., adisplay device, printer, or speaker). Such a system may also include oneor more storage devices, such as disk drives, optical storage devices,and solid-state storage devices such as random-access memory (RAM) orread-only memory (ROM), as well as removable media devices, memorycards, flash cards, etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.), and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed, and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting, and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services, or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets), or both. Further, connection to other computing devicessuch as network input/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer readable instructions, data structures,program modules, or other data, including RAM, ROM, ElectricallyErasable Programmable Read-Only Memory (EEPROM), flash memory or othermemory technology, Compact Disc-Read Only Memory (CD-ROM), DigitalVersatile Disk (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by a system device. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

In the preceding description, various embodiments are described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described.

Bracketed text and blocks with dashed borders (e.g., large dashes, smalldashes, dot-dash, and dots) are used herein to illustrate optionaloperations that add additional features to some embodiments. However,such notation should not be taken to mean that these are the onlyoptions or optional operations, and/or that blocks with solid bordersare not optional in certain embodiments.

Reference numerals with suffix letters (e.g., 918A-918N) may be used toindicate that there can be one or multiple instances of the referencedentity in various embodiments, and when there are multiple instances,each does not need to be identical but may instead share some generaltraits or act in common ways. Further, the particular suffixes used arenot meant to imply that a particular amount of the entity exists unlessspecifically indicated to the contrary. Thus, two entities using thesame or different suffix letters may or may not have the same number ofinstances in various embodiments.

References to “one embodiment,” “an embodiment,” “an exampleembodiment,” etc., indicate that the embodiment described may include aparticular feature, structure, or characteristic, but every embodimentmay not necessarily include the particular feature, structure, orcharacteristic. Moreover, such phrases are not necessarily referring tothe same embodiment. Further, when a particular feature, structure, orcharacteristic is described in connection with an embodiment, it issubmitted that it is within the knowledge of one skilled in the art toaffect such feature, structure, or characteristic in connection withother embodiments whether or not explicitly described.

Moreover, in the various embodiments described above, unlessspecifically noted otherwise, disjunctive language such as the phrase“at least one of A, B, or C” is intended to be understood to mean eitherA, B, or C, or any combination thereof (e.g., A, B, and/or C). As such,disjunctive language is not intended to, nor should it be understood to,imply that a given embodiment requires at least one of A, at least oneof B, or at least one of C to each be present.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the disclosure asset forth in the claims.

What is claimed is:
 1. A computer-implemented method comprising: displaying a first video frame of a scene received from a video camera; receiving a user selection of a first sub-region of the first video frame, the user selection based on a drawing of a boundary of the first sub-region, the first sub-region having a location and an area in the first video frame; receiving an indication of a first machine learning model to associate with the first sub-region, the first machine learning model trained to detect an instance of a first type of real-world object; receiving a user selection of a second sub-region of the first video frame, the second sub-region having a location and an area in the first video frame; receiving an indication of a second machine learning model to associate with the second sub-region, the second machine learning model trained to detect an instance of a second type of real-world object; receiving an indication of a third machine learning model to associate with the second sub-region, the third machine learning model trained to detect an instance of a third type of real-world object; receiving a second video frame of the scene from the video camera; identifying a third sub-region of the second video frame corresponding in a location and an area in the second video frame to the location and the area of the first sub-region in the first video frame; selecting the first machine learning model to analyze the third sub-region based on the receiving the indication of the first machine learning model to associate with the first sub-region; analyzing the third sub-region of the second video frame using the first machine learning model; identifying a fourth sub-region of the second video frame corresponding in a location and an area in the second video frame to the location and the area of the second sub-region in the first video frame; selecting the second machine learning model to analyze the fourth sub-region based on the receiving the indication of the second machine learning model to associate with the second sub-region; selecting the third machine learning model to analyze the fourth sub-region based on the receiving the indication of the third machine learning model to associate with the second sub-region; analyzing the fourth sub-region of the second video frame using the second machine learning model; analyzing the fourth sub-region of the second video frame using the third machine learning model; and outputting a result of the analyzing, the result indicating whether an instance of the first type of real-world object is detected in the third sub-region, the result further indicating whether an instance of the second type of real-world object is detected in the fourth sub-region, and the result also indicating whether an instance of the third type of real-world object is detected in the fourth sub-region.
 2. The computer-implemented method of claim 1, wherein the third sub-region is cropped from the second video frame; and analyzing the third sub-region using the first machine learning model is performed using the third sub-region as cropped from the second video frame.
 3. The computer-implemented method of claim 1, further comprising: cropping the fourth sub-region out of the second video frame; and wherein the analyzing the fourth sub-region using the second machine learning model is performed using the fourth sub-region as cropped out of the second video frame.
 4. The computer-implemented method of claim 1, further comprising: extracting the fourth sub-region from the second video frame; and wherein the analyzing the fourth sub-region using the second machine learning model is performed using the fourth sub-region as extracted from the second video frame.
 5. The computer-implemented method of claim 1, wherein the first video frame is a wide-angle digital image.
 6. The computer-implemented method of claim 1, further comprising: pre-processing the fourth sub-region by performing one or more of color enhancement, up-sampling, down-sampling, and contrast enhancement.
 7. The computer-implemented method of claim 1, wherein the user selection of the first sub-region indicates a set of coordinates of the first video frame; and wherein identifying the third sub-region of the second video frame corresponding to the first sub-region comprises determining a set of coordinates of the second video frame corresponding to the set of coordinates of the first video frame.
 8. The computer-implemented method of claim 1, wherein the scene comprises an entry to and an exit from a building; and wherein the first sub-region corresponds to the entry to the building and the second sub-region corresponds to the exit from the building.
 9. The computer-implemented method of claim 1, wherein the first sub-region and the second sub-region partially overlap.
 10. The computer-implemented method of claim 1, wherein the selection of the second sub-region indicates a set of coordinates of the first video frame; and wherein identifying the fourth sub-region of the second video frame corresponding to the second sub-region comprises determining a set of coordinates of the second video frame corresponding to the set of coordinates of the first video frame.
 11. The computer-implemented method of claim 1, wherein the scene comprises a conveyor belt; and wherein the first sub-region and the second sub-region correspond to different portions of the conveyor belt of the scene.
 12. The computer-implemented method of claim 1, further comprising: based on the result, sending an alert to a person, the alert indicating that an instance of the first type of real-world object is detected in the third sub-region.
 13. A system comprising: an image capture device to capture one or more images; and one or more electronic devices to implement an image analysis service, the image analysis service including instructions that upon execution cause the image analysis service to: display a first digital image of a scene, the first digital image captured by the image capture device; receive a user-selection of a first sub-region of the first digital image of the scene, wherein the first sub-region has a location and an area in the first digital image; receive a user-selection indicating a first machine learning model to associate with the first sub-region, the first machine learning model trained to detect a first type of real-world object; receive a user-selection of a second sub-region of the first digital image of the scene, wherein the second sub-region has a location and an area in the first digital image; receive a user-selection indicating a second machine learning model to associate with the second sub-region, the second machine learning model trained to detect a second type of real-world object; receive a user-selection indicating a third machine learning model to associate with the second sub-region, the third machine learning model trained to detect a third type of real-world object; identify a third sub-region of a second digital image of the scene corresponding in a location and an area in the second digital image to the location and the area of the first sub-region in the first digital image, the second digital image captured by the image capture device; select the first machine learning model to analyze the third sub-region based on the user-selection indicating the third machine learning model to associate with the first sub-region; analyze the third sub-region using the first machine learning model associated with the first sub-region; identify a fourth sub-region of the second digital image corresponding in a location and an area in the second digital image to the location and the area of the second sub-region in the first digital image; select the second machine learning model to analyze the fourth sub-region based on the user-selection indicating the second machine learning model to associate with the second sub-region; select the third machine learning model to analyze the fourth sub-region based on the user-selection indicating the third machine learning model to associate with the second sub-region; analyze the third sub-region of the second digital image using the second machine learning model; analyze the fourth sub-region of the second digital image using the third machine learning model; and output a result indicating whether an instance of the first type of real-world object is detected in the third sub-region by the first machine learning model, the result further indicating whether an instance of the second type of real-world object is detected in fourth sub-region by the second machine learning model, and the result also indicating whether an instance of the third type of real-world object is detected in the fourth sub-region by the third machine learning model.
 14. The system of claim 13, wherein the image analysis service is to pre-process the third sub-region by performing one or more of color enhancement, up-sampling, down-sampling, and contrast enhancement.
 15. The system of claim 13, wherein the third sub-region is extracted from the second digital image; and wherein the instructions of the image analysis service that upon execution cause the image analysis service to analyze the third sub-region using the second machine learning model comprise instructions that upon execution cause the image analysis service to analyze the third sub-region using the second machine learning model as extracted from the second digital image.
 16. The system of claim 13, wherein the third sub-region is cropped from the second digital image; and wherein the instructions of the image analysis service that upon execution cause the image analysis service to analyze the third sub-region using the second machine learning model comprise instructions that upon execution cause the image analysis service to analyze the third sub-region using the second machine learning model as cropped from the second digital image.
 17. The system of claim 13, wherein the image capture device is a wide-angle camera.
 18. The system of claim 13, wherein the user selection of the first sub-region indicates a first set of coordinates of the first digital image; and wherein the instructions of the image analysis service that upon execution cause the image analysis service to identify the third sub-region of the second digital image corresponding to the first sub-region comprise instructions that upon execution cause the image analysis service to determine a second set of coordinates of the second digital image corresponding to the first set of coordinates.
 19. The system of claim 13, wherein the one or more electronic devices to implement the image analysis service comprises the image capture device.
 20. The system of claim 13, wherein the image capture device comprises the one or more electronic devices to implement the image analysis service. 